A Denoising Algorithm Combined with EMD and LMS for Precise Transmission Signal

IF 1.2 4区工程技术 Q3 ACOUSTICS

Shock and Vibration Pub Date : 2023-12-31 DOI:10.1155/2023/8853345

Lei Song, Yongjun Cao, Yushan Zhou, Dongdong You

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引用次数: 0

Abstract

High accuracy and stability in mechanical transmission are crucial for various applications. In spite of the validity of mechanical enhancements, control algorithms’ fulfilment offers a cost-effective and efficient approach to mitigating the effects of noise signals. This study presents a hybrid algorithm that combines EMD with the least mean square (LMS) error to achieve online denoising. Within the algorithm, consecutive mean square error (CMSE) and the 2-norm metric are employed to assess the similarity between intrinsic mode functions (IMFs) and the original signal; therefore, IMFs are separated into three distinct components: noise components, information components, and mixed components. The denoised signal is obtained by partial reconstruction. Subsequently, the denoised signal is employed as a reference signal in the LMS algorithm, which is utilized for practical processing. The performance evaluation of the developed algorithm employs simulation and experimental signals. The obtained results illustrate that the presented approach achieves sufficient accuracy and stability.

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一种结合 EMD 和 LMS 的去噪算法，用于精确传输信号

机械传动的高精度和高稳定性对各种应用都至关重要。尽管机械增强的有效性，但控制算法的实现为减轻噪声信号的影响提供了一种经济有效的方法。本研究提出了一种混合算法，将 EMD 与最小均方误差 (LMS) 结合起来，实现在线去噪。在该算法中，连续均方误差（CMSE）和 2-norm 指标被用来评估本征模态函数（IMF）与原始信号之间的相似性；因此，IMF 被分成三个不同的成分：噪声成分、信息成分和混合成分。通过部分重建获得去噪信号。随后，去噪信号被用作 LMS 算法中的参考信号，用于实际处理。对所开发算法的性能评估采用了模拟和实验信号。结果表明，所提出的方法具有足够的准确性和稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Shock and Vibration 物理-工程：机械

CiteScore

3.40

自引率

6.20%

发文量

384

审稿时长

3 months

期刊介绍： Shock and Vibration publishes papers on all aspects of shock and vibration, especially in relation to civil, mechanical and aerospace engineering applications, as well as transport, materials and geoscience. Papers may be theoretical or experimental, and either fundamental or highly applied.